Optimizing the Location of Virtual-Shopping-Experience Stores Based on the Minimum Impact on Urban Traffic

In order to enhance consumers’ experience of online shopping and reduce their unnecessary car trips for offline shopping, a new mode, namely, establishing the virtual-shopping-experience store, is proposed in this paper. A bi-level programming model then built with aim optimizing location stores. The upper-level submodel utilized optimize stores, as well selection virtual-reality (VR) devices purchased by maximizing social welfare generated from reducing after establishment lower-level binary Logit model, one which calculates probability choices between according locations stores output submodel. genetic algorithm adopted solve model. To validate accuracy that algorithm, case studies are carried out based on real data collected Dalian Ningbo (two cities China). study result demonstrates would contribute frequency distance time spent shopping.